Exponential synchronization criteria for Markovian jumping neural networks with time-varying delays and sampled-data control

Abstract

This paper deals with the problem of exponential synchronization of Markovian jumping neural networks with time-varying delays and variable sampling control. Several delay-dependent synchronization criteria are derived to ensure the convergence of the error systems, that is, the master systems stochastically synchronized with the slave systems. By employing an improved Lyapunov–Krasovskii functional (LKF) with the triple integral terms and combining the convex technique, two new sufficient conditions are derived to guarantee that a class of delayed neural networks (DNNs) to be globally exponentially stable. The information about the lower bound of the discrete time-varying delay is fully used in the LKF. Moreover, the conditions obtained in this paper are formulated in terms of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. The maximum sampling intervals are obtained based on the design of mode-independent controller. Finally, three numerical examples are given to demonstrate the efficiency of the proposed theoretical results.